Biology subjects

Scott, L.

Publications and source records attributed to Scott, L..

At least 19 recordsLinked to original sources

bioRxiv2026

Erosion of regenerative regulation: age-associated shifts in the skeletal muscle fiber epigenome and transcriptome

Skeletal muscle aging is characterized by the deterioration of muscle function, which can lead to negative quality-of-life outcomes including frailty and sarcopenia. While understanding the mechanisms of this process is increasingly important as the global population ages, previous molecular studies of skeletal muscle aging have been limited by statistical power and cell type resolution. In this study, we analyzed single-nucleus gene expression and chromatin accessibility data from 287 human skeletal muscle samples from individuals aged 20-79 years to explore sex- and cell type- specific aging effects. Across 467,126 nuclei from 13 cell types, we identify 384 age-associated genes and 4,061 age-associated chromatin regions. These age-associated molecular features are enriched for functional pathways, including metabolic processes, cell-to-cell communication, and senescence Kyoto Encyclopedia of Genes and Genomes KEGG terms. Age-associated closing chromatin was more common across fiber types and sexes than opening chromatin, and was enriched in active enhancer regions while depleted for active transcription start sites. We observe enrichment for specific transcription factor motifs in closing chromatin, including those of glucocorticoid and androgen receptors, both of which play a key role in the maintenance of healthy skeletal muscle. Together, these findings identify an age-associated regulatory shift, largely invisible in matched transcriptomic data, characterized by closing chromatin which reduces accessibility to hormone receptor binding sites and enhancer regions in the muscle fiber epigenome.

bioinformatics↗

bioRxiv2026

Integrated Analysis of Skeletal Muscle Transcriptional Networks Characterizes Dysregulation in Pathways and Trait-Associated Regulatory Regions in Type 2 Diabetes

Skeletal muscle, a primary site of insulin-mediated glucose uptake, plays a central role in the pathogenesis of type 2 diabetes. It is therefore critical to understand the disease-associated alterations in skeletal muscle and identify the underlying drivers of this dysregulation. Here, we characterize type 2 diabetes associated transcriptional dysregulation using 301 skeletal muscle biopsies from living donors with and without diabetes. Using weighted gene co-expression network analysis, we identify 56 distinct gene modules, which we further characterize using single-nucleus RNA-seq-derived cell type signatures and pathway enrichment analysis. We identify numerous cell type-associated dysregulated pathways in skeletal muscle tissue from individuals with diabetes, including muscle fiber-associated mitochondrial function and mRNA splicing and processing; endothelial vascularization and phospholipase D signaling; and macrophage- and T-cell-associated inflammation. Through analysis of module hub genes and transcription factor regulatory network analysis, we further identify candidate driver genes of this dysregulation including ATP5L, ATF2, SIRT1, and THRAP3 in muscle fibers; JAM2 and CLEC14A in endothelial cells; and F13A1 and IRF8 in immune cells. Finally, we integrate our co-expression networks with single-nucleus ATAC-seq data to identify proximal and distal genomic regulatory elements and identify context-specific enrichment for type 2 diabetes and related trait GWAS signals in muscle fiber and endothelial modules. Together, our results reveal dysregulation in pathways in muscle tissue from individuals with diabetes, identify candidate drivers, and connect the genomic drivers of this dysregulation across type 2 diabetes and related metabolic traits.

bioinformatics↗

bioRxiv2025

{Phi}X174 bacteriophage viability predicted by protein biophysical modeling

The relationship between genotype and phenotype underlies our ability to understand and predict evolution. Efforts to build genotype-phenotype (GP) maps have revealed several unifying rules: epistasis is pervasive, fitness effects are not normally distributed, and the GP map is non-linear and complicated for high-level phenotypes. A critical step in developing GP maps is evaluating how well predictive models do in explaining observed phenotypes. We utilize the simplicity of a bacteriophage ({Phi}X174) study system to test if an intermediate phenotype (predicted stability of the G capsid protein) explains more complex phenotypes. In doing so, we compare the predictive performance of free energies of folding and binding obtained using numerous molecular modeling methods as well as phylogenetic and basic biochemical/biophysical properties of amino acid substitutions. By creating a large mutational library, we find that {Phi}X174 tolerates about 50% of the amino acid substitutions we inserted into the G protein and that molecular modeling compliments other substitution models for predicting viability. Mutations predicted to have large destabilizing effects are especially informative and are almost universally detrimental. These large-effect substitutions often coincide with the most conserved residues in the G protein. Apart from large-effect mutations, our ability to predict {Phi}X174 phenotypes is fairly poor and we explore various potential confounding factors (e.g., codon bias) that could be considered to improve viability predictions.

molecular biology↗

bioRxiv2025

Age-related changes in the gut microbiota of a long-lived seabird suggest divergence from mammalian models

Gut microbiota plays key roles in shaping host development, immunity, and physiology. The assembly of vertebrate gut microbiota during early life follows relatively predictable trajectories across several mammalian species, largely driven by maternal transmission routes. However, birds lack these transmission mechanisms due to their oviparity, potentially leading to different age-related microbiota patterns. Here, we investigate gut microbiota variation in Atlantic puffin (Fratercula arctica) chicks and adults across six British populations using 16S rRNA amplicon sequencing. We find that chick microbiotas show strong population-level structuring that disappears in adults, with spatial patterns particularly pronounced in anaerobic bacteria. Puffin microbiotas become increasingly individualised and aerotolerant with age, while taxonomic richness remains stable - patterns that contrast with the convergent, richness-increasing trajectories observed in several mammalian species. Our results suggest that environmental exposure drives early-life microbiota assembly in birds, highlighting the value of avian systems for understanding vertebrate microbiota development beyond mammals.

ecology↗

bioRxiv2025

Machine Learning-Driven Optimization of Specific, Compact, and Efficient Base Editors via Single-Round Diversification

Base editing shows great potential in research and clinical applications. Current iterations of the deaminases used to create precise single-nucleotide changes via base editing exhibit undesirable effects, including off-targeting, off-base editing, and bystander editing. Current deaminases are derived from either larger eukaryotic deaminases, which exhibit high levels of Cas-independent DNA targeting, or from evolved variants of the smaller E. coli TadA protein (ecTadA), which exhibits off-base editing. To overcome the limitations inherent to using a single protein sequence for engineering, we diversified newly identified TadA orthologs by DNA shuffling to yield millions of training sequences for measuring base editor efficiency. We trained generative models on the performance data from the pools of variants and drew on information-theoretic insights to efficiently explore the sequence space to generate diverse and high-performing deaminases. From a single round of diversification, we created a small set of novel and specific cytosine and adenosine deaminases that were markedly distinct in sequence from published base editor deaminases. We found that our model created deaminases generally outperform those we identified through typical directed evolution. The novel compact deaminases identified here show high on-base activity, comparable to the leading published base editors, and with demonstrably lower off-base activity. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=89 SRC="FIGDIR/small/666807v2_ufig1.gif" ALT="Figure 1"> View larger version (39K): org.highwire.dtl.DTLVardef@cd25c1org.highwire.dtl.DTLVardef@69ad98org.highwire.dtl.DTLVardef@412e4org.highwire.dtl.DTLVardef@1744100_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract C_FIG

bioengineering↗

bioRxiv2023

Replenishing Age-Related Decline of IRAK-M Expression in Retinal Pigment Epithelium Attenuates Outer Retinal Degeneration

Unchecked, chronic inflammation is a constitutive component of age-related diseases, including age-related macular degeneration (AMD). Here we identified interleukin-1 receptor-associated kinase (IRAK)-M as a key immunoregulator in retinal pigment epithelium (RPE) that declines with age. Rare genetic variants of IRAK-M increased the likelihood of AMD. IRAK-M expression in RPE declined with age or oxidative stress and was further reduced in AMD. IRAK-M-deficient mice exhibited increased incidence of outer retinal degeneration at earlier ages, which was further exacerbated by oxidative stressors. The absence of IRAK-M disrupted RPE cell homeostasis, including compromised mitochondrial function, cellular senescence, and aberrant cytokine production. IRAK-M overexpression protected RPE cells against oxidative or immune stressors. Subretinal delivery of AAV-expressing IRAK-M rescued light-induced outer retinal degeneration in wild-type mice and attenuated age-related spontaneous retinal degeneration in IRAK-M- deficient mice. Our data support that replenishment of IRAK-M expression may redress dysregulated pro-inflammatory processes in AMD, thereby treating degeneration. One Sentence SummaryIRAK-M is a protective molecule and promising therapeutic target for macular degeneration

immunology↗

bioRxiv2023

Proteome-Wide Association Studies for Blood Lipids and Comparison with Transcriptome-Wide Association Studies

Blood lipid traits are treatable and heritable risk factors for heart disease, a leading cause of mortality worldwide. Although genome-wide association studies (GWAS) have discovered hundreds of variants associated with lipids in humans, most of the causal mechanisms of lipids remain unknown. To better understand the biological processes underlying lipid metabolism, we investigated the associations of plasma protein levels with total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL), and low-density lipoprotein cholesterol (LDL) in blood. We trained protein prediction models based on samples in the Multi-Ethnic Study of Atherosclerosis (MESA) and applied them to conduct proteome-wide association studies (PWAS) for lipids using the Global Lipids Genetics Consortium (GLGC) data. Of the 749 proteins tested, 42 were significantly associated with at least one lipid trait. Furthermore, we performed transcriptome-wide association studies (TWAS) for lipids using 9,714 gene expression prediction models trained on samples from peripheral blood mononuclear cells (PBMCs) in MESA and 49 tissues in the Genotype-Tissue Expression (GTEx) project. We found that although PWAS and TWAS can show different directions of associations in an individual gene, 40 out of 49 tissues showed a positive correlation between PWAS and TWAS signed p-values across all the genes, which suggests a high-level consistency between proteome-lipid associations and transcriptome-lipid associations.

genomics↗

bioRxiv2022

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 studies with 88,316 MD cases and 902,757 controls to previously reported data from individuals of European ancestry. This includes samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latinx participants (32%). The multi-ancestry GWAS identified 190 significantly associated loci, 53 of them novel. For previously reported loci from GWAS in European ancestry the power-adjusted transferability ratio was 0.6 in the Hispanic/Latinx group and 0.3 in each of the other groups. Fine-mapping benefited from additional sample diversity: the number of credible sets with [≤]5 variants increased from 3 to 12. A transcriptome-wide association study identified 354 significantly associated genes, 205 of them novel. Mendelian Randomisation showed a bidirectional relationship with BMI exclusively in samples of European ancestry. This first multi-ancestry GWAS of MD demonstrates the importance of large diverse samples for the identification of target genes and putative mechanisms.

genetics↗

bioRxiv2022

Rapid and specific degradation of endogenous proteins in mouse models using auxin-inducible degrons

Auxin-inducible degrons are a chemical genetic tool for targeted protein degradation and are widely used to study protein function in cultured mammalian cells. Here, we develop CRISPR-engineered mouse lines that enable rapid and highly specific degradation of tagged endogenous proteins in vivo. Most but not all cell types are competent for degradation. Using mouse genetics, we show that degradation kinetics depend upon the dose of the tagged protein, ligand, and the E3 ligase subunit Tir1. Rapid degradation of condensin I and condensin II - two essential regulators of mitotic chromosome structure - revealed that both complexes are individually required for cell division in precursor lymphocytes, but not in their differentiated peripheral lymphocyte derivatives. This generalisable approach provides unprecedented temporal control over the dose of endogenous proteins in mouse models, with implications for studying essential biological pathways and modelling drug activity in mammalian tissues. HighlightsO_LIAuxin-inducible degradation of endogenously tagged proteins in living mice and a range of primary cells. C_LIO_LIMost but not all cell types are competent for degradation C_LIO_LIDosage of the tagged protein, E3 ligase substrate receptor and ligand can all determine degradation kinetics C_LIO_LIRapid degradation of condensin subunits in lymphocytes reveals stage-specific requirements during cell division C_LI

genetics↗

bioRxiv2021

De novo germline mutation in the Dual Specificity Phosphatase 10 gene accelerates autoimmune diabetes in Non-Obese Diabetic (NOD) mice

Here we report the isolation by selective breeding of two sublines of Non-Obese Diabetic (NOD) mice exhibiting a significant difference in the incidence of autoimmune type 1 diabetes (T1D). Whole genome sequencing of the NOD/NckH (high T1D incidence) and NOD/NckL (low T1D incidence) revealed the presence of a limited number of variants specific to each subline. Treating the age of T1D onset as a quantitative trait and using automated meiotic mapping (AMM), enhanced susceptibility in the NOD/NckH subline was unambiguously attributed to a recessive allele of Dusp10 which encodes a dual specificity phosphatase. The causative effect of the mutation was verified with a high level of confidence by targeting Dusp10 with CRISPR/Cas9 in NOD/NckL mice: in these animals a higher incidence of diabetes was observed. Expression of wild-type Dusp10 correlated with higher levels of surface PD-L1 in the islets of NOD/NckL mice.

immunology↗

bioRxiv2020

A versatile toolkit for molecular QTL mapping and meta-analysis at scale

Molecular QTLs (xQTLs) are widely studied to identify functional variation and possible mechanisms underlying genetic associations with diseases. Larger xQTL sample sizes are critical to help identify causal variants, improve predictive models, and increase power to detect rare associations. This will require scalable and accurate methods for analysis of tens of thousands of molecular traits in large cohorts, and/or from summary statistics in meta-analysis, both of which are currently lacking. We developed APEX (All-in-one Package for Efficient Xqtl analysis), an efficient toolkit for xQTL mapping and meta-analysis that provides (a) highly optimized linear mixed models to account for relatedness and shared variation across molecular traits; (b) rapid factor analysis to infer latent technical and biological variables from molecular trait data; (c) fast and accurate trait-level omnibus tests that incorporate prior functional weights to increase statistical power; and (d) compact summary data files for flexible and accurate joint analysis of multiple variants (e.g., joint/conditional regression or Bayesian finemapping) without individual-level data in meta-analysis. We applied the methods to data from three LCL eQTL studies and the UK Biobank. APEX is open source: https://corbinq.github.io/apex.

genetics↗

bioRxiv2020

Candidate Explorer: a tool for discovery, evaluation, and display of mutations causing significant immune phenotypes

When applied to immunity, forward genetic studies use meiotic mapping to provide strong statistical evidence that a particular mutation is causative of a particular immune phenotype. Notwithstanding this, co-segregation of multiple mutations, occasional unawareness of mutations, and paucity of homozygotes may lead to erroneous declarations of cause and effect. We sought to improve the selection of authentic causative mutations using a machine learning software tool, Candidate Explorer (CE), which integrates 65 data features into a single numeric score, mathematically convertible to the likelihood of verification of any putative mutation-phenotype association. CE has identified most genes within which mutations can be causative of flow cytometric phenovariation in Mus musculus. The majority of these genes were not previously known to support immune function or homeostasis. Mouse geneticists will find CE data informative in identifying causative mutations within quantitative trait loci, while clinical geneticists may use CE to help connect causative variants with rare heritable diseases of immunity, even in the absence of linkage information. CE displays integrated mutation, phenotype, and linkage data, and is freely available for query online.

genetics↗

bioRxiv2020

Developmental regulation of mitotic chromosome formation revealed by condensin reporter mice

Withdrawal Note: The authors have withdrawn their manuscript due to the discovery of artifacts that call into question key findings from the study. Both artifacts originate from cell-type differences that arose during sample processing, creating the impression that non-SMC subunits of condensin proteins were present at substantially higher levels in thymic T cells compared to bone marrow B cells or erythroblast cells. Based on data collected since the preprint was submitted, we no longer believe this to be true. The experimental artifacts do not affect the data contributed by the Papadopoulos laboratory (Figure 4). The authors wish to make it clear that all data and experimental protocols in the original manuscript were accurately reported, and that the experimental artifacts which affect data interpretation were discovered during internal review, independently of peer review, and are being reported at the authors own instigation. They do not, to our knowledge, affect the interpretation of data from any other published work. Due to the issues outlined above, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

cell biology↗

bioRxiv2020

{Phi}X174 Attenuation by Whole Genome Codon Deoptimization

Natural selection acting on synonymous mutations in protein-coding genes influences genome composition and evolution. In viruses, introducing synonymous mutations in genes encoding structural proteins can drastically reduce viral growth, providing a means to generate potent, live attenuated vaccine candidates. However, an improved understanding of what compositional features are under selection and how combinations of synonymous mutations affect viral growth is needed to predictably attenuate viruses and make them resistant to reversion. We systematically recoded all non-overlapping genes of the bacteriophage {Phi}X174 with codons rarely used in its E. coli host. The fitness of recombinant viruses decreases as additional deoptimizing mutations are made to the genome, although not always linearly, and not consistently across genes. Combining deoptimizing mutations may reduce viral fitness more or less than expected from the effect size of the constituent mutations and we point out difficulties in untangling correlated compositional features. We test our model by optimizing the same genes and find that the relationship between codon usage and fitness does not hold for optimization, suggesting that wild-type {Phi}X174 is at a fitness optimum. This work highlights the need to better understand how selection acts on patterns of synonymous codon usage across the genome and provides a convenient system to investigate the genetic determinants of virulence.

genomics↗

bioRxiv2019

A powerful subset-based gene-set analysis method identifies novel associations and improves interpretation in UK Biobank

Tests of association between a phenotype and a set of genes in a biological pathway can provide insights into the genetic architecture of complex phenotypes beyond those obtained from single variant or single gene association analysis. However, most existing gene set tests have limited power to detect gene set-phenotype association when a small fraction of the genes are associated with the phenotype, and no method exists which identifies the potentially "active" genes that might drive a gene-set-based association. To address these issues, we have developed Gene-set analysis Association Using Sparse Signals (GAUSS), a method for gene-set association analysis that requires only GWAS summary statistics. For each significantly associated gene set, GAUSS identifies the subset of genes that have the maximal evidence of association and can best account for the gene set association. Using pre-computed correlation structure among test statistics from a reference panel, our p-value calculation is substantially faster than other permutation or simulation-based approaches. In simulations with varying proportions of causal genes, we find that GAUSS effectively controls type 1 error rate and has greater power than several existing methods, particularly when a small proportion of genes account for the gene set signal. Using GAUSS, we analyzed UK Biobank GWAS summary statistics for 10,679 gene-sets and 1,403 binary phenotypes. We found that GAUSS is scalable and identified 13,466 phenotype and gene-set association pairs. Within these genes sets, we identify an average of 17.2 (max=405) genes that underlie these gene set associations.

genetics↗

bioRxiv2019

Genomic Prediction of Depression Risk and Resilience Under Stress

Advancing our ability to predict who is likely to develop depression in response to stress holds great potential in reducing the burden of the disorder. Large-scale genome-wide association studies (GWAS) of depression have, for the first time, provided a basis for meaningful depression polygenic risk score construction (MDD-PRS). The Intern Health Study utilizes the predictable and large increase in depression with physician training stress to identify predictors of depression. Applying the MDD-PRS derived from the PGC2/23andMe GWAS to 5,227 training physicians, we found that MDD-PRS predicted depression under training stress (beta=0.082, p=2.1x10-12) and that MDD-PRS was significantly more strongly associated with depression under stress than at baseline (MDD-PRS x stress interaction - beta=0.029, p=0.02). While known risk factors accounted for 85.6% of the association between MDD-PRS and depression at baseline, they only accounted for 55.4% of the association between MDD-PRS and depression under stress, suggesting that MDD-PRS can add unique predictive power to existing models of depression under stress. Further, we found that low MDD-PRS may have particular utility in identifying individuals with high resilience. Together, these findings suggest that polygenic risk score holds promise in furthering our ability to predict vulnerability and resilience under stress.

genomics↗

bioRxiv2018

Bioinformatic analysis reveals mechanisms underlying parasitoid venom evolution and function

Introduction Introduction Methods Results and Discussion Conclusions References Parasitoid wasps are a numerous and diverse group of insects that obligately infect other arthropod species. These wasps lay their eggs either on the surface or within the body cavity of their hosts, and the resulting offspring exploit the hosts resources to complete their development [1]. Many parasitoids also introduce venom gland derived proteins or polydnaviruses into the host during infection. These factors act through a variety of mechanisms to manipulate host biology in order to increase the fitness of the developing parasitoid offspring [2-4]. Many hosts mount immune responses to parasitoid infection, and accordingly, parasitoids have evolved multiple venom genes that encode immunom ...

genomics↗

bioRxiv2018

Genetic discovery and translational decision support from exome sequencing of 20,791 type 2 diabetes cases and 24,440 controls from five ancestries

Protein-coding genetic variants that strongly affect disease risk can provide important clues into disease pathogenesis. Here we report an exome sequence analysis of 20,791 type 2 diabetes (T2D) cases and 24,440 controls from five ancestries. We identify rare (minor allele frequency<0.5%) variant gene-level associations in (a) three genes at exome-wide significance, including a T2D-protective series of >30 SLC30A8 alleles, and (b) within 12 gene sets, including those corresponding to T2D drug targets (p=6.1x10-3) and candidate genes from knockout mice (p=5.2x10-3). Within our study, the strongest T2D rare variant gene-level signals explain at most 25% of the heritability of the strongest common single-variant signals, and the rare variant gene-level effect sizes we observe in established T2D drug targets will require 110K-180K sequenced cases to exceed exome-wide significance. To help prioritize genes using associations from current smaller sample sizes, we present a Bayesian framework to recalibrate association p-values as posterior probabilities of association, estimating that reaching p<0.05 (p<0.005) in our study increases the odds of causal T2D association for a nonsynonymous variant by a factor of 1.8 (5.3). To help guide target or gene prioritization efforts, our data are freely available for analysis at www.type2diabetesgenetics.org.

genetics↗